In the last installment of the Dataflow course series, we will introduce the components of the Dataflow operational model. We will examine tools and techniques for troubleshooting and optimizing pipeline performance. We will then review testing, deployment, and reliability best practices for Dataflow pipelines. We will conclude with a review of Templates, which makes it easy to scale Dataflow pipelines to organizations with hundreds of users. These lessons will help ensure that your data platform is stable and resilient to unanticipated circumstances.
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Reviews
3.6 (17 ratings)
- 5 stars47.05%
- 4 stars11.76%
- 3 stars11.76%
- 2 stars11.76%
- 1 star17.64%
RR
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Labs are keeping up-to-date, but are lacking overall theoretical summary to teach symmatically how each code could work. Still a very typical problem of courses offered by Google Cloud.
RT
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Good intermediate course covering the big picture about how to develop data platforms using GCP and Dataflow.
From the lesson
Logging and Error Reporting
In this module, we learn how to use the Log panel at the bottom of both the Job Graph and Job Metrics pages, and learn about the centralized Error Reporting page.
Taught By
Google Cloud Training